Computational protocol: NanoString expression profiling identifies candidate biomarkers of RAD001 response in metastatic gastric cancer

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Protocol publication

[…] Total RNA was extracted from 5 to 10 sections of 4 μm thick FFPE sections as previously described. Non-tumour elements were removed by manual microdissection before transferring to the extraction tube guided by H&E-stained slides. Total RNA was then extracted using the High Pure RNA Paraffin kit (Roche Diagnostic, Mannheim, Germany) or E.Z.N.A. FFPE RNA Isolation Kit (Omega Bio-Tek, Norcross, Georgia, USA) according to the manufacturer's protocol. Concentrations of extracted RNA were determined using the NanoDrop 8000 Spectrophotometer (Thermo Scientific). The samples with RNA concentrations of <40 ng/μL, A260/A280 ratios <1.5 or A260/230 ratios <1.0 were considered as inadequate and were excluded from the analysis.A NanoString panel was designed, comprising 495 previously published genes representing 10 different GC-related oncogenic signalling and molecular subtype-specific expression signatures of clinical relevance (see online supplementary table S1). Additionally, five housekeeping genes (GAPDH, TBP, ACTB, RPL29 and GUSB) showing minimal alteration across GC samples were also included as controls. The custom-designed probes included a 100-bp region targeting the mRNA, with two sequence-specific, fluorescent-barcoded probes for each target (3’ biotinylated capture probe and a 5’ reporter probe). Probes and 100 ng total RNA were hybridised overnight at 65°C according to the manufacturer's protocol. A NanoString nCounter Digital Analyzer (NanoString Technologies, Seattle, Washington, USA) was used to count the digital barcodes representing the number of transcripts. The raw expression data were normalised using nSolver Analysis software. A normalisation factor was calculated by obtaining the geometric mean of the positive controls used for each sample and applied to the raw counts of the nCounter output data to eliminate variability that was unrelated to the samples. The resulting data were normalised again with the geometric mean of the housekeeping genes. Normalised data were log2-transformed for further analyses. During the data normalisation process, we found that 18 genes exhibited expression values below the limits of detection as defined by NanoString nSolver Analysis software. These 18 genes were subsequently filtered out and discarded, leaving a total of 477 genes for downstream analysis. [...] Individual gene sets were divided into 10 Signature Groups. The normalised log2-transformed mRNA expression data of 54 tumours were analysed by unsupervised hierarchical clustering using Cluster V.3.0 and Java Tree view software. Heat maps showing high and low expression of genes in the subtypes were generated and tumours were categorised on the basis of these expression patterns. Statistical analysis was performed using IBM-SPSS Statistics V.22.0 for Windows (SPSS Inc, Chicago, Illinois, USA). Gene expression levels were correlated against different clusters of tumours using a non-parametric, several independent samples test, and a p value <0.05 was considered significant by the Kruskal-Wallis H test. Fisher's exact test was used to evaluate correlations between gene expression patterns or clusters and clinicopathological characteristics. An independent samples comparison of the means Student t test was used to determine correlations between single-gene expression levels and patient clinicopathological parameters and response to treatment. Overall survival and PFS analysis was performed using the Kaplan-Meier survival analysis and a p value <0.05 was considered significant (log-rank test). Univariate and multivariate analyses with regard to treatment were performed using a Cox proportional hazards regression model. All p values were adjusted using a false discovery rate (FDR) correction of 5% for multiple comparisons. All tests were two-sided at the significance level p<0.05. PFS was defined as the time from initiation of the RAD001 treatment to the date of documented disease progression or death from any cause. […]

Pipeline specifications

Software tools nSolver Analysis Software, SPSS
Applications nCounter System, Miscellaneous
Organisms Homo sapiens
Diseases Adenocarcinoma, Neoplasms, Stomach Neoplasms, Carcinoma, Signet Ring Cell